Multiphase MRI‐Based Radiomics for Predicting Histological Grade of Hepatocellular Carcinoma

• Published in: Journal of magnetic resonance imaging 2024-11, Vol.60 (5), p.2117-2127
• Main Authors: Yan, Yang, Si, Zhang, Chun, Cui, Chao‐qun, Pen, Ke, Mu, Dong, Zhang, Li, Wen
• Format: Article
• Language: English
• Subjects: Calibration; Cancer; Chi-square test; Cirrhosis; Correlation coefficient; Correlation coefficients; Diethylenetriamine pentaacetic acid; Field strength; Gadolinium; Gd‐EOB‐DTPA; Hepatitis B; Hepatocellular carcinoma; histological grade; Histopathology; Liver cancer; Magnetic resonance imaging; Multiphase; Performance evaluation; Performance prediction; Radiomics; Rank tests; Regression analysis; Statistical analysis; Statistical models; Statistical tests; Tumors
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.29289

Background Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer. Accurate preoperative prediction of histological grade holds potential for improving clinical management and disease prognostication. Purpose To evaluate the performance of a radiomics signature based on multiphase MRI in assessing histological grade in solitary HCC. Study Type Retrospective. Subjects A total of 405 patients with histopathologically confirmed solitary HCC and with liver gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd‐EOB‐DTPA)‐enhanced MRI within 1 month of surgery. Field Strength/Sequence Contrast‐enhanced T1‐weighted spoiled gradient echo sequence (LAVA) at 1.5 or 3.0 T. Assessment Tumors were graded (low/high) according to results of histopathology. Basic clinical characteristics (including age, gender, serum alpha‐fetoprotein (AFP) level, history of hepatitis B, and cirrhosis) were collected and tumor size measured. Radiomics features were extracted from Gd‐EOB‐DTPA‐enhanced MRI data. Three feature selection strategies were employed sequentially to identify the optimal features: SelectFromModel (SFM), SelectPercentile (SP), and recursive feature elimination with cross‐validation (RFECV). Probabilities of five single‐phase radiomics‐based models were averaged to generate a radiomics signature. A combined model was built by combining the radiomics signature and clinical predictors. Statistical Tests Pearson χ2 test/Fisher exact test, Wilcoxon rank sum test, interclass correlation coefficient (ICC), univariable/multivariable logistic regression analysis, area under the receiver operating characteristic (ROC) curve (AUC), DeLong test, calibration curve, Brier score, decision curve, Kaplan–Meier curve, and log‐rank test. A P‐value 5 cm; OR 2.33) were significantly associated with HCC grade. The combined model had excellent performance in assessing HCC grade in the test dataset (AUC: 0.801), and demonstrated satisfactory calibration and clinical utility. Data Conclusion A model that combined a radiomics signature derived from preoperative multiphase Gd‐EOB‐DTPA‐enhanced MRI and clinical predictors showed good performance in assessing HCC grade. Level of Evidence 3 Technical Efficacy Stage 5